Updated 29/11/2017: I am adding AI programming to ramp up my Python skills and some focus into a gamification site, codingame.com. I have updated the article to reflect this.
Call it as you want.. it is a very fuzzy topic and there are many discussions on the names and concepts 😉
Since from some time, after the “death” of Silverlight, I had an empty space… which was to me the DRIVE, to me this is something exciting that gets me engaged, that pushes and motivates me to go further… it’s when you are in a hackathon and you have this feeling of…
This is it!!
And even .NET Core is an exciting thing with its .NET Standard compliance, And Azure is pretty exciting and improving on a day to day basis, they were not still bringing that “shiny” “Silverlight” factor that pushed me to play and explore with that technology and make it my playground… to devour design and interaction books as well as physics programming just to optimize resources and get to do magic in the UI… what times!!
So, I had two candidates: ML/DS (Machine Learning / Data Science) and AR/VR/MR… and the second is still not mature enough (and it was impossible to get a HoloLens too) I decided earlier this year to go for Machine Learning 🙂 – even you probably have figured it out after reading the title..
I have set up a path on this vast topic which is Data Science, Machine Learning and AI. And, on this path, to learn the best tools for the task in front..
That said, I already worked 2+ years in ETL (Extract, Transform, Load) to prepare data in a big editorial, as well as in BI & reporting… as other knowledge I can leverage from my experience..
But what is Data Science exactly? (as well as those other buzzwords)
As my understanding goes, these are their meanings/areas:
- Data Science – The “all goes in” discipline, collecting the data, organizing it, preparing for searching patterns in the data to be able to make advanced “tasks” on it like predictions, classification, etc.. Usually this tasks are the work of a Machine Learning Model that does the magic. Usually this profile has a decent background in Data management, and in defining data flows to integrate the data in a repository where the automated analysis can be done. Also this task requires Math and statistic skills.
- Machine Learning – Science of creating (or adapting/tuning) algorithms that learn on their own from data (read: can be trained to perform better). Usually a mixed profile of a Matemathician & coder fits this position the best. To say that ML is a subset of Data Science.
- Deep Learning: To most people, this is a subset of Machine Learning, which are in fact a ML technique (neural networks). Which has had a lot of success in certain problems and is becoming a discipline on its own.
- AI – Subfield of Computer Science to program computers that solve human tasks, so they can performing planning, moving, recognizing objects, etc… basically any task. This includes ML as making a prediction on a set of data is, basically, a task. That makes ML a subset of AI, basically. ML has as a goal to make computers handle the task of learning upon data and by themselves, so they can make predictions.
And even I believe this is a clear description, there are people still discussing about this definitions… Here are some more articles that discuss this topic in way more detail, like this one. If you want to understand how wide are the possibilities for a “data scientist” read this.
Some people have several different but similar opinions, and if you have time, you can read some of them. But…
I want to feel the power of DS/ML in my fingertips, know from the top to the bottom how to get things done understanding every single step and to be able to design, code and tune complex models that provide accurate results.. and to be able to explain those models through proper visualizations that provide a clear insight of the decision taken by the model.. And for this,
I have a plan…
Here is My path forward for DS-ML-DL…
Step A: become a Data Science / ML “begginer”
Goal: to become knowledgeable of what is “out there” what is the people using, what are the main technologies and get a feeling on them. Also I
likeLove UI and believe that the proper presentation helps greatly to understanding so want to invest a good deal in data presentation skills.
- Andy Ng’s Machine Learning – done! – great base but everything done with Mathlab… and no excessive explanation as the exercises were pre-prepared.
- Udemy introduction to Data Science – done
- EDX program from Microsoft for Data Science– in progress (4 out of 11 courses)
- Tableau A to Z (done)
Step B: become a proficient, or at least intermediate, ML developer and DS practitioner:
Goal: To become competent in programming with a hands-on practical approach, both in R and in Python even I believe I will dig in deeper with Python as there is a lot more material in there.
- Datacamp.com practicing with some courses in Python, 2 modules completed.
- codingame.com practicing to polish my AI agent coding skills (in Python), currently implementing the “intermediate” challenges.
- Python A-Z (udemy, Kirill Eremenko) (Done!)
- R a-z (udemy, Kirill Eremenko)
- Machine Learning A-Z, hands-on Phython & R (Udemy)
- Taming Big Data with Apache Spark & Phyton. (Udemy)
Step C: Become intermediate to advance ML Developer and get some experience:
Goal: do I need to explain? 😉
- Ensemble ML
- Start digging in on Kaggle, on examples and tutorials to get up to speed and compete in at least one Data Science contest. Ref: https://www.kaggle.com/
Kaggle is a ML “professional” racing competition so I want to have some ground skills and “driving” experience before joining a competition.
- I want this experience to consolidate my learnings all together with hands on experience, with a goal.
- Tableau expert top visualization techniques (to get some better knowledge of Tableau)
Step D: Get DEEP.
Goal: To get into the most deep and complex topic on today’s Machine Learning panorama, Deep Learning with the new computational advances seems to be key in implementing new approaches of predictive systems, and more – they are being used to develop AI systems able to develop strategies that beat the best humans at a task, to be creative as humans can be, but without our limitations – limited cpu power, limited ability to learn and procrastination.. I have setup the following courses
- Deep Learning A-Z
- Artificial Intelligence A-Z
- Join some Kaggle challenges regarding DL and-or AI development.
- Deep Learning: GANs and variational Autoencoders
- Bayesian Machine Learning in Python
- Cluster Analysis and unsupervised ML
Obviously this is a vast topic and things can evolve there or change…